momepy.Neighbors¶

class momepy.Neighbors(gdf, spatial_weights, unique_id, weighted=False)[source]¶

Calculate the number of neighbours captured by spatial_weights

If weighted=True, number of neighbours will be divided by the perimeter of object to return relative value.

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Parameters

gdfGeoDataFrame

GeoDataFrame containing objects to analyse

spatial_weightslibpysal.weights

spatial weights matrix

unique_idstr

name of the column with unique id used as spatial_weights index

weightedbool (default False)

if weighted=True, number of neighbours will be divided by the perimeter of object, to return relative value

References

Hermosilla T, Ruiz LA, Recio JA, et al. (2012) Assessing contextual descriptive features for plot-based classification of urban areas. Landscape and Urban Planning, Elsevier B.V. 106(1): 124–137.

Examples

>>> sw = libpysal.weights.contiguity.Queen.from_dataframe(tessellation_df, ids='uID')
>>> tessellation_df['neighbours'] = momepy.Neighbors(tessellation_df, sw, 'uID').series
100%|██████████| 144/144 [00:00<00:00, 6909.50it/s]
>>> tessellation_df['neighbours'][0]
4

Attributes

seriesSeries

Series containing resulting values

gdfGeoDataFrame

original GeoDataFrame

valuesSeries

Series containing used values

swlibpysal.weights

spatial weights matrix

idSeries

Series containing used unique ID

weightedbool

used weighted value

__init__(self, gdf, spatial_weights, unique_id, weighted=False)[source]¶

Initialize self. See help(type(self)) for accurate signature.

Methods

__init__(self, gdf, spatial_weights, unique_id)

Initialize self.